{
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  "metadata": {
    "colab": {
      "name": "Tabulation of the Performance of the Martingale on the NASDAQ-100.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMhOK9wQ72Eq1HvILmBIQHY",
      "include_colab_link": true
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    }
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Farmhouse121/Adventures-in-Financial-Data-Science/blob/main/Tabulation_of_the_Performance_of_the_Martingale_on_the_NASDAQ_100.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install yfinance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QBgDPhRiozDD",
        "outputId": "339c7064-60db-4e73-bdca-82dc50561236"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.69-py2.py3-none-any.whl (26 kB)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.19.5)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)\n",
            "Collecting lxml>=4.5.1\n",
            "  Downloading lxml-4.7.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.4 MB 10.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.1.5)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)\n",
            "\u001b[K     |████████████████████████████████| 63 kB 1.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance) (1.15.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.0.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2021.10.8)\n",
            "Installing collected packages: requests, lxml, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed lxml-4.7.1 requests-2.27.1 yfinance-0.1.69\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from yfinance import download\n",
        "\n",
        "# this list created 2021-12-26, you must use Adjusted Close \n",
        "# not simple Close to account for splits/dividends etc\n",
        "prices=download([\"AAPL\", \"ABNB\", \"ADBE\", \"ADI\", \"ADP\", \"ADSK\", \"AEP\", \"ALGN\", \"AMAT\", \"AMD\", \"AMGN\", \"AMZN\", \"ANSS\", \"ASML\", \"ATVI\", \"AVGO\", \"BIDU\", \"BIIB\", \"BKNG\", \"CDNS\", \n",
        "                 \"CHTR\", \"CMCSA\", \"COST\", \"CPRT\", \"CRWD\", \"CSCO\", \"CSX\", \"CTAS\", \"CTSH\", \"DDOG\", \"DLTR\", \"DOCU\", \"DXCM\", \"EA\", \"EBAY\", \"EXC\", \"FAST\", \"FB\", \"FISV\", \"FTNT\", \n",
        "                 \"GILD\", \"GOOG\", \"GOOGL\", \"HON\", \"IDXX\", \"ILMN\", \"INTC\", \"INTU\", \"ISRG\", \"JD\", \"KDP\", \"KHC\", \"KLAC\", \"LCID\", \"LRCX\", \"LULU\", \"MAR\", \"MCHP\", \"MDLZ\", \"MELI\", \n",
        "                 \"MNST\", \"MRNA\", \"MRVL\", \"MSFT\", \"MTCH\", \"MU\", \"NFLX\", \"NTES\", \"NVDA\", \"NXPI\", \"OKTA\", \"ORLY\", \"PANW\", \"PAYX\", \"PCAR\", \"PDD\", \"PEP\", \"PTON\", \"PYPL\", \"QCOM\", \n",
        "                 \"REGN\", \"ROST\", \"SBUX\", \"SGEN\", \"SIRI\", \"SNPS\", \"SPLK\", \"SWKS\", \"TEAM\", \"TMUS\", \"TSLA\", \"TXN\", \"VRSK\", \"VRSN\", \"VRTX\", \"WBA\", \"WDAY\", \"XEL\", \"XLNX\", \"ZM\", \n",
        "                 \"ZS\"])[\"Adj Close\"] \n",
        "prices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 701
        },
        "id": "NHk5FHrwotbv",
        "outputId": "57d37108-5685-46de-af42-312539508879"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  101 of 101 completed\n"
          ]
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-01-05</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.004756</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-01-08</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.997145</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-14</th>\n",
              "      <td>173.070007</td>\n",
              "      <td>163.990005</td>\n",
              "      <td>520.599976</td>\n",
              "      <td>172.000000</td>\n",
              "      <td>228.750000</td>\n",
              "      <td>259.100006</td>\n",
              "      <td>90.980003</td>\n",
              "      <td>524.630005</td>\n",
              "      <td>167.000000</td>\n",
              "      <td>136.880005</td>\n",
              "      <td>235.360001</td>\n",
              "      <td>3242.760010</td>\n",
              "      <td>348.540009</td>\n",
              "      <td>744.530029</td>\n",
              "      <td>65.389999</td>\n",
              "      <td>596.369995</td>\n",
              "      <td>154.529999</td>\n",
              "      <td>239.300003</td>\n",
              "      <td>2450.949951</td>\n",
              "      <td>161.750000</td>\n",
              "      <td>607.690002</td>\n",
              "      <td>51.680000</td>\n",
              "      <td>502.989990</td>\n",
              "      <td>136.710007</td>\n",
              "      <td>176.699997</td>\n",
              "      <td>61.360001</td>\n",
              "      <td>36.439999</td>\n",
              "      <td>397.450012</td>\n",
              "      <td>87.199997</td>\n",
              "      <td>138.279999</td>\n",
              "      <td>130.809998</td>\n",
              "      <td>130.440002</td>\n",
              "      <td>447.339996</td>\n",
              "      <td>130.440002</td>\n",
              "      <td>63.450001</td>\n",
              "      <td>56.240002</td>\n",
              "      <td>59.200001</td>\n",
              "      <td>331.899994</td>\n",
              "      <td>109.110001</td>\n",
              "      <td>312.540009</td>\n",
              "      <td>...</td>\n",
              "      <td>204.729996</td>\n",
              "      <td>83.000000</td>\n",
              "      <td>310.200012</td>\n",
              "      <td>121.150002</td>\n",
              "      <td>97.360001</td>\n",
              "      <td>525.690002</td>\n",
              "      <td>103.360001</td>\n",
              "      <td>269.420013</td>\n",
              "      <td>225.289993</td>\n",
              "      <td>204.410004</td>\n",
              "      <td>678.869995</td>\n",
              "      <td>499.109985</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>96.500000</td>\n",
              "      <td>60.410000</td>\n",
              "      <td>175.639999</td>\n",
              "      <td>31.330000</td>\n",
              "      <td>178.419998</td>\n",
              "      <td>188.690002</td>\n",
              "      <td>615.239990</td>\n",
              "      <td>102.269997</td>\n",
              "      <td>100.120003</td>\n",
              "      <td>137.089996</td>\n",
              "      <td>6.18</td>\n",
              "      <td>326.160004</td>\n",
              "      <td>122.620003</td>\n",
              "      <td>155.649994</td>\n",
              "      <td>297.390015</td>\n",
              "      <td>108.519997</td>\n",
              "      <td>1049.609985</td>\n",
              "      <td>187.009995</td>\n",
              "      <td>198.149994</td>\n",
              "      <td>226.479996</td>\n",
              "      <td>234.059998</td>\n",
              "      <td>54.299999</td>\n",
              "      <td>254.309998</td>\n",
              "      <td>68.970001</td>\n",
              "      <td>198.449997</td>\n",
              "      <td>159.720001</td>\n",
              "      <td>254.740005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-18</th>\n",
              "      <td>169.800003</td>\n",
              "      <td>154.690002</td>\n",
              "      <td>513.340027</td>\n",
              "      <td>166.500000</td>\n",
              "      <td>226.440002</td>\n",
              "      <td>252.029999</td>\n",
              "      <td>90.320000</td>\n",
              "      <td>489.559998</td>\n",
              "      <td>152.360001</td>\n",
              "      <td>131.929993</td>\n",
              "      <td>233.830002</td>\n",
              "      <td>3178.350098</td>\n",
              "      <td>337.540009</td>\n",
              "      <td>715.229980</td>\n",
              "      <td>82.309998</td>\n",
              "      <td>577.789978</td>\n",
              "      <td>152.940002</td>\n",
              "      <td>233.809998</td>\n",
              "      <td>2384.209961</td>\n",
              "      <td>153.529999</td>\n",
              "      <td>582.750000</td>\n",
              "      <td>50.180000</td>\n",
              "      <td>488.070007</td>\n",
              "      <td>132.940002</td>\n",
              "      <td>172.850006</td>\n",
              "      <td>59.730000</td>\n",
              "      <td>35.250000</td>\n",
              "      <td>391.059998</td>\n",
              "      <td>86.190002</td>\n",
              "      <td>131.869995</td>\n",
              "      <td>129.039993</td>\n",
              "      <td>127.309998</td>\n",
              "      <td>429.869995</td>\n",
              "      <td>133.910004</td>\n",
              "      <td>61.610001</td>\n",
              "      <td>56.040001</td>\n",
              "      <td>58.389999</td>\n",
              "      <td>318.149994</td>\n",
              "      <td>106.400002</td>\n",
              "      <td>308.470001</td>\n",
              "      <td>...</td>\n",
              "      <td>186.610001</td>\n",
              "      <td>77.669998</td>\n",
              "      <td>302.649994</td>\n",
              "      <td>115.870003</td>\n",
              "      <td>92.870003</td>\n",
              "      <td>510.799988</td>\n",
              "      <td>101.739998</td>\n",
              "      <td>259.029999</td>\n",
              "      <td>217.419998</td>\n",
              "      <td>197.199997</td>\n",
              "      <td>665.669983</td>\n",
              "      <td>502.369995</td>\n",
              "      <td>123.029999</td>\n",
              "      <td>95.970001</td>\n",
              "      <td>61.529999</td>\n",
              "      <td>173.960007</td>\n",
              "      <td>30.230000</td>\n",
              "      <td>174.460007</td>\n",
              "      <td>178.860001</td>\n",
              "      <td>611.219971</td>\n",
              "      <td>99.209999</td>\n",
              "      <td>97.730003</td>\n",
              "      <td>130.289993</td>\n",
              "      <td>6.11</td>\n",
              "      <td>313.480011</td>\n",
              "      <td>118.050003</td>\n",
              "      <td>151.649994</td>\n",
              "      <td>293.630005</td>\n",
              "      <td>106.910004</td>\n",
              "      <td>1030.510010</td>\n",
              "      <td>182.279999</td>\n",
              "      <td>198.449997</td>\n",
              "      <td>220.100006</td>\n",
              "      <td>231.070007</td>\n",
              "      <td>53.520000</td>\n",
              "      <td>245.860001</td>\n",
              "      <td>68.389999</td>\n",
              "      <td>187.800003</td>\n",
              "      <td>157.699997</td>\n",
              "      <td>259.200012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-19</th>\n",
              "      <td>166.229996</td>\n",
              "      <td>154.580002</td>\n",
              "      <td>516.580017</td>\n",
              "      <td>163.059998</td>\n",
              "      <td>224.830002</td>\n",
              "      <td>254.350006</td>\n",
              "      <td>90.300003</td>\n",
              "      <td>497.589996</td>\n",
              "      <td>143.070007</td>\n",
              "      <td>128.270004</td>\n",
              "      <td>231.300003</td>\n",
              "      <td>3125.979980</td>\n",
              "      <td>337.100006</td>\n",
              "      <td>698.820007</td>\n",
              "      <td>82.150002</td>\n",
              "      <td>563.969971</td>\n",
              "      <td>154.479996</td>\n",
              "      <td>230.809998</td>\n",
              "      <td>2377.090088</td>\n",
              "      <td>153.169998</td>\n",
              "      <td>581.299988</td>\n",
              "      <td>50.330002</td>\n",
              "      <td>490.160004</td>\n",
              "      <td>131.899994</td>\n",
              "      <td>175.710007</td>\n",
              "      <td>58.900002</td>\n",
              "      <td>35.250000</td>\n",
              "      <td>387.839996</td>\n",
              "      <td>86.040001</td>\n",
              "      <td>130.910004</td>\n",
              "      <td>131.320007</td>\n",
              "      <td>126.989998</td>\n",
              "      <td>435.230011</td>\n",
              "      <td>136.910004</td>\n",
              "      <td>62.290001</td>\n",
              "      <td>56.490002</td>\n",
              "      <td>58.020000</td>\n",
              "      <td>319.589996</td>\n",
              "      <td>106.050003</td>\n",
              "      <td>307.160004</td>\n",
              "      <td>...</td>\n",
              "      <td>174.070007</td>\n",
              "      <td>76.889999</td>\n",
              "      <td>303.329987</td>\n",
              "      <td>116.269997</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>515.859985</td>\n",
              "      <td>103.459999</td>\n",
              "      <td>250.669998</td>\n",
              "      <td>210.949997</td>\n",
              "      <td>197.419998</td>\n",
              "      <td>659.590027</td>\n",
              "      <td>519.609985</td>\n",
              "      <td>121.269997</td>\n",
              "      <td>94.160004</td>\n",
              "      <td>64.349998</td>\n",
              "      <td>175.210007</td>\n",
              "      <td>31.840000</td>\n",
              "      <td>173.550003</td>\n",
              "      <td>172.470001</td>\n",
              "      <td>618.109985</td>\n",
              "      <td>99.029999</td>\n",
              "      <td>96.870003</td>\n",
              "      <td>132.550003</td>\n",
              "      <td>6.10</td>\n",
              "      <td>312.140015</td>\n",
              "      <td>118.779999</td>\n",
              "      <td>145.880005</td>\n",
              "      <td>293.459991</td>\n",
              "      <td>105.379997</td>\n",
              "      <td>995.650024</td>\n",
              "      <td>178.300003</td>\n",
              "      <td>199.860001</td>\n",
              "      <td>217.240005</td>\n",
              "      <td>230.839996</td>\n",
              "      <td>53.529999</td>\n",
              "      <td>249.460007</td>\n",
              "      <td>68.120003</td>\n",
              "      <td>189.089996</td>\n",
              "      <td>157.229996</td>\n",
              "      <td>253.110001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-20</th>\n",
              "      <td>164.509995</td>\n",
              "      <td>158.000000</td>\n",
              "      <td>510.850006</td>\n",
              "      <td>158.630005</td>\n",
              "      <td>220.309998</td>\n",
              "      <td>252.410004</td>\n",
              "      <td>89.919998</td>\n",
              "      <td>490.709991</td>\n",
              "      <td>139.149994</td>\n",
              "      <td>121.889999</td>\n",
              "      <td>228.899994</td>\n",
              "      <td>3033.350098</td>\n",
              "      <td>333.320007</td>\n",
              "      <td>706.460022</td>\n",
              "      <td>81.760002</td>\n",
              "      <td>547.719971</td>\n",
              "      <td>162.029999</td>\n",
              "      <td>225.910004</td>\n",
              "      <td>2433.639893</td>\n",
              "      <td>150.309998</td>\n",
              "      <td>572.159973</td>\n",
              "      <td>50.220001</td>\n",
              "      <td>482.820007</td>\n",
              "      <td>128.399994</td>\n",
              "      <td>173.889999</td>\n",
              "      <td>58.080002</td>\n",
              "      <td>35.240002</td>\n",
              "      <td>380.299988</td>\n",
              "      <td>85.690002</td>\n",
              "      <td>130.009995</td>\n",
              "      <td>127.970001</td>\n",
              "      <td>124.440002</td>\n",
              "      <td>434.640015</td>\n",
              "      <td>138.880005</td>\n",
              "      <td>60.709999</td>\n",
              "      <td>56.980000</td>\n",
              "      <td>56.169998</td>\n",
              "      <td>316.559998</td>\n",
              "      <td>106.300003</td>\n",
              "      <td>304.989990</td>\n",
              "      <td>...</td>\n",
              "      <td>167.520004</td>\n",
              "      <td>73.800003</td>\n",
              "      <td>301.600006</td>\n",
              "      <td>118.660004</td>\n",
              "      <td>85.070000</td>\n",
              "      <td>508.250000</td>\n",
              "      <td>103.360001</td>\n",
              "      <td>241.500000</td>\n",
              "      <td>199.610001</td>\n",
              "      <td>200.320007</td>\n",
              "      <td>648.059998</td>\n",
              "      <td>519.669983</td>\n",
              "      <td>120.989998</td>\n",
              "      <td>92.300003</td>\n",
              "      <td>66.120003</td>\n",
              "      <td>173.940002</td>\n",
              "      <td>24.219999</td>\n",
              "      <td>173.279999</td>\n",
              "      <td>166.500000</td>\n",
              "      <td>614.380005</td>\n",
              "      <td>97.430000</td>\n",
              "      <td>95.720001</td>\n",
              "      <td>133.059998</td>\n",
              "      <td>6.04</td>\n",
              "      <td>309.959991</td>\n",
              "      <td>118.099998</td>\n",
              "      <td>142.990005</td>\n",
              "      <td>291.320007</td>\n",
              "      <td>104.059998</td>\n",
              "      <td>996.270020</td>\n",
              "      <td>173.449997</td>\n",
              "      <td>198.029999</td>\n",
              "      <td>216.529999</td>\n",
              "      <td>228.610001</td>\n",
              "      <td>53.180000</td>\n",
              "      <td>251.770004</td>\n",
              "      <td>68.180000</td>\n",
              "      <td>185.610001</td>\n",
              "      <td>155.809998</td>\n",
              "      <td>250.669998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-21</th>\n",
              "      <td>162.410004</td>\n",
              "      <td>156.729996</td>\n",
              "      <td>499.910004</td>\n",
              "      <td>159.520004</td>\n",
              "      <td>217.130005</td>\n",
              "      <td>239.190002</td>\n",
              "      <td>89.970001</td>\n",
              "      <td>462.779999</td>\n",
              "      <td>135.059998</td>\n",
              "      <td>118.809998</td>\n",
              "      <td>227.720001</td>\n",
              "      <td>2852.860107</td>\n",
              "      <td>325.739990</td>\n",
              "      <td>694.729980</td>\n",
              "      <td>81.349998</td>\n",
              "      <td>533.229980</td>\n",
              "      <td>156.839996</td>\n",
              "      <td>220.520004</td>\n",
              "      <td>2345.860107</td>\n",
              "      <td>148.300003</td>\n",
              "      <td>569.690002</td>\n",
              "      <td>49.730000</td>\n",
              "      <td>481.609985</td>\n",
              "      <td>126.690002</td>\n",
              "      <td>164.729996</td>\n",
              "      <td>56.680000</td>\n",
              "      <td>34.099998</td>\n",
              "      <td>379.510010</td>\n",
              "      <td>84.260002</td>\n",
              "      <td>125.550003</td>\n",
              "      <td>126.230003</td>\n",
              "      <td>116.129997</td>\n",
              "      <td>422.100006</td>\n",
              "      <td>139.009995</td>\n",
              "      <td>59.540001</td>\n",
              "      <td>56.770000</td>\n",
              "      <td>56.520000</td>\n",
              "      <td>303.170013</td>\n",
              "      <td>104.860001</td>\n",
              "      <td>288.649994</td>\n",
              "      <td>...</td>\n",
              "      <td>160.070007</td>\n",
              "      <td>72.550003</td>\n",
              "      <td>296.029999</td>\n",
              "      <td>115.839996</td>\n",
              "      <td>81.930000</td>\n",
              "      <td>397.500000</td>\n",
              "      <td>96.989998</td>\n",
              "      <td>233.740005</td>\n",
              "      <td>199.949997</td>\n",
              "      <td>188.259995</td>\n",
              "      <td>635.919983</td>\n",
              "      <td>483.630005</td>\n",
              "      <td>118.669998</td>\n",
              "      <td>91.559998</td>\n",
              "      <td>62.410000</td>\n",
              "      <td>174.220001</td>\n",
              "      <td>27.059999</td>\n",
              "      <td>163.539993</td>\n",
              "      <td>164.929993</td>\n",
              "      <td>621.400024</td>\n",
              "      <td>95.430000</td>\n",
              "      <td>96.309998</td>\n",
              "      <td>127.550003</td>\n",
              "      <td>6.01</td>\n",
              "      <td>303.309998</td>\n",
              "      <td>115.279999</td>\n",
              "      <td>139.899994</td>\n",
              "      <td>283.000000</td>\n",
              "      <td>101.620003</td>\n",
              "      <td>943.900024</td>\n",
              "      <td>175.639999</td>\n",
              "      <td>196.089996</td>\n",
              "      <td>217.139999</td>\n",
              "      <td>228.080002</td>\n",
              "      <td>52.500000</td>\n",
              "      <td>245.690002</td>\n",
              "      <td>68.220001</td>\n",
              "      <td>187.789993</td>\n",
              "      <td>147.660004</td>\n",
              "      <td>241.940002</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>15120 rows × 101 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d59aabbb-690d-4420-b0b2-004ac7244255')\"\n",
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              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-d59aabbb-690d-4420-b0b2-004ac7244255');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  AAPL        ABNB  ...          ZM          ZS\n",
              "Date                                ...                        \n",
              "1962-01-02         NaN         NaN  ...         NaN         NaN\n",
              "1962-01-03         NaN         NaN  ...         NaN         NaN\n",
              "1962-01-04         NaN         NaN  ...         NaN         NaN\n",
              "1962-01-05         NaN         NaN  ...         NaN         NaN\n",
              "1962-01-08         NaN         NaN  ...         NaN         NaN\n",
              "...                ...         ...  ...         ...         ...\n",
              "2022-01-14  173.070007  163.990005  ...  159.720001  254.740005\n",
              "2022-01-18  169.800003  154.690002  ...  157.699997  259.200012\n",
              "2022-01-19  166.229996  154.580002  ...  157.229996  253.110001\n",
              "2022-01-20  164.509995  158.000000  ...  155.809998  250.669998\n",
              "2022-01-21  162.410004  156.729996  ...  147.660004  241.940002\n",
              "\n",
              "[15120 rows x 101 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "areturn,correl=pd.DataFrame(),{}\n",
        "\n",
        "for ticker in prices:\n",
        "    p,q=prices[ticker],prices[ticker].shift()\n",
        "    areturn[ticker]=abs((p-q)/p)*1e2\n",
        "    correl[ticker]=pd.DataFrame({0:p,1:q}).corr().loc[0,1]*1e2\n",
        "\n",
        "scores=pd.DataFrame({\n",
        "    \"MAPE\":areturn.mean(axis=0),\n",
        "    \"RMSPE\":areturn.apply(lambda x:x*x).mean(axis=0).apply(np.sqrt),\n",
        "    \"R²\":pd.DataFrame({\"Correlation\":correl.values()},index=correl.keys())[\"Correlation\"].apply(lambda x:x*x/1e2)\n",
        "\n",
        "})\n",
        "scores"
      ],
      "metadata": {
        "id": "jHpRMg5DpSkS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "b2b96ba0-ee63-4057-8095-e6328badad40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b2a9cecd-bc14-4d6e-9a24-84c1ea6ec775\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MAPE</th>\n",
              "      <th>RMSPE</th>\n",
              "      <th>R²</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AAPL</th>\n",
              "      <td>1.954763</td>\n",
              "      <td>2.997943</td>\n",
              "      <td>99.957328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ABNB</th>\n",
              "      <td>2.495090</td>\n",
              "      <td>3.356695</td>\n",
              "      <td>91.354574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ADBE</th>\n",
              "      <td>2.067971</td>\n",
              "      <td>3.127927</td>\n",
              "      <td>99.942176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ADI</th>\n",
              "      <td>1.906305</td>\n",
              "      <td>2.773019</td>\n",
              "      <td>99.926120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ADP</th>\n",
              "      <td>1.139935</td>\n",
              "      <td>1.678411</td>\n",
              "      <td>99.963973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WDAY</th>\n",
              "      <td>1.689802</td>\n",
              "      <td>2.422955</td>\n",
              "      <td>99.726020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XEL</th>\n",
              "      <td>0.883706</td>\n",
              "      <td>1.476112</td>\n",
              "      <td>99.967544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XLNX</th>\n",
              "      <td>2.156006</td>\n",
              "      <td>3.136221</td>\n",
              "      <td>99.862225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZM</th>\n",
              "      <td>2.838248</td>\n",
              "      <td>4.158657</td>\n",
              "      <td>99.218184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZS</th>\n",
              "      <td>2.549003</td>\n",
              "      <td>3.691166</td>\n",
              "      <td>99.705172</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>101 rows × 3 columns</p>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          MAPE     RMSPE         R²\n",
              "AAPL  1.954763  2.997943  99.957328\n",
              "ABNB  2.495090  3.356695  91.354574\n",
              "ADBE  2.067971  3.127927  99.942176\n",
              "ADI   1.906305  2.773019  99.926120\n",
              "ADP   1.139935  1.678411  99.963973\n",
              "...        ...       ...        ...\n",
              "WDAY  1.689802  2.422955  99.726020\n",
              "XEL   0.883706  1.476112  99.967544\n",
              "XLNX  2.156006  3.136221  99.862225\n",
              "ZM    2.838248  4.158657  99.218184\n",
              "ZS    2.549003  3.691166  99.705172\n",
              "\n",
              "[101 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as pl\n",
        "golden_ratio=(1e0+np.sqrt(5e0))/2e0\n",
        "figure,plots=pl.subplots(1,3,figsize=(15*golden_ratio,15/3))\n",
        "title='Distributions of Forecasting Metrics for NASDAQ–100 Stocks'\n",
        "figure.suptitle(title,fontsize=20)\n",
        "\n",
        "for score in scores:\n",
        "    plot=plots[list(scores).index(score)]\n",
        "    scores[score].hist(ax=plot,bins=np.linspace(min(scores[score]),max(scores[score]),50))\n",
        "    plot.set_xlabel(score,fontsize=14)\n",
        "    plot.set_ylabel(\"Frequency\",fontsize=14)\n",
        "\n",
        "scores.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "u5NEUJsVpdTr",
        "outputId": "b10a7663-209a-465a-cddb-903bab9601d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6586004c-8821-4a27-9732-a877cd8ad457\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MAPE</th>\n",
              "      <th>RMSPE</th>\n",
              "      <th>R²</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>101.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>101.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.949706</td>\n",
              "      <td>2.965117</td>\n",
              "      <td>99.733931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.596417</td>\n",
              "      <td>0.947107</td>\n",
              "      <td>0.935439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.880444</td>\n",
              "      <td>1.312120</td>\n",
              "      <td>91.354574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.534590</td>\n",
              "      <td>2.245941</td>\n",
              "      <td>99.843236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.957346</td>\n",
              "      <td>3.030257</td>\n",
              "      <td>99.909653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.279190</td>\n",
              "      <td>3.539580</td>\n",
              "      <td>99.944140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.901999</td>\n",
              "      <td>6.770020</td>\n",
              "      <td>99.975645</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6586004c-8821-4a27-9732-a877cd8ad457')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6586004c-8821-4a27-9732-a877cd8ad457 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6586004c-8821-4a27-9732-a877cd8ad457');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             MAPE       RMSPE          R²\n",
              "count  101.000000  101.000000  101.000000\n",
              "mean     1.949706    2.965117   99.733931\n",
              "std      0.596417    0.947107    0.935439\n",
              "min      0.880444    1.312120   91.354574\n",
              "25%      1.534590    2.245941   99.843236\n",
              "50%      1.957346    3.030257   99.909653\n",
              "75%      2.279190    3.539580   99.944140\n",
              "max      3.901999    6.770020   99.975645"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3PB+2x1K+ENZazPs2JSnbKq5LMvPWFtOvqJ53aJg21/p/BLAN8JPMvLHF/G9Vz/2sv5dmZrR4rKnmP7ap7D/KzJuAH1F6XO3RYtutLu1+QvX8oCjjrG7wYH2ysz427R6U5MdPqwTwjKKMVfvVKGPq3lUfTxL4a0qP3/s2LH4Cpc4viogPRcRBEdHNDym9OrfFtFbn0GOq5+9l68vOz55jHJ+ifJ68rLoM+mWUuu10GX79mD2lzTG7PyUx9/AeY/kDpZd3t/aunr/exbInVM/nRMQnIuL5EbFrT9F11ul98QtKT+jdW7z/e93nduo37np/F8s+jfKjyulNCfp68nRp848e1Q8nyyjDNCylXE7/A+ChlEv5L4iI58wy9o3as4ZzcYMkdPU5+FVK7/79Z1leo62q8g5qdS43PB4z04Y2ZZn508z8c8oQOYcDX6AMkbMPZZiPcyJihw6baNZr+9nLe7VuivLD6eOAF2fm+5oXqNrir1LGiP5xlPHWnxrlhnqSJA2EY/BKkvoiIraijNcIcEOnZSl32r6MMnboiuqxLiL+h9Jr7NIei7925kVaajdOb317c0k6z9Z2wI3Z4i7qmbkuIupjrzZrN55tvVdzY6+yudZ/vV6uaTO/Pn3GXlx9NJeYWp0/O1XPfzdDufWe0fXtXtVuwUYRcRhl7MabgNMpl9DfTkkm1cdu/mPvtcz8YHXsXwO8gTLmakbEt4G3Vj3P+2Gj86g672DDc6he3+3eQ92Mgd1WZp4fEedTztHvU4bgeP0Mq9WP2VtnWK65N/tMrm/spd6Frs+FzDy5SkAup/Q+fCVANabrEZl5eo+xNuvmffGnlJgbe2X2us8tZeb3IuIk4LkR8fzsPK75H8e1bdrGjRHxVeBv2Xhc5voy11HGzT0e/tjD8l8ovTU/ExG7tvpMnUH9pnyN7dkzKefiN5t7iVdx/221H6dW0+qfLTtFxNadevFGuZlb/Ryul3kQ62/W2coaNh6Tvlv1492unatPb/xMmM06M6o+v/74GRYRj6ccy70oV2q8sc2qrcrvpf3s6XO78gBKr98r6fxD1vMpvYZfxPqxtP9QvR/eUp2zkiT1jT14JUn98iTKD4fXNfSqbCkz787MD2fmXpTeMH9LuTHTAcA3ZnFZ5mwTEVNtpj+gem5MeNR7Kbb6cbSficxbgB1bXJ5PRCyg9Ops1VO3a32o/3q9PKDN/J2blhuGWcfUJpFVX+7ANj2H64/6F/d6QmPGS6ur43gUJfnzqMx8fma+NTOPzMyjaJMczcz/yMy9KUmg/SmXNO8DfHNAvXk7qZ+D7d5D7ab3YiWlPj9BGcLgczMs/8fk0wzHbKNLsWfQ6+dL1+cCQGauysynUXpILwE+RBmL9GsR8cgey2422/fFnJO7DY6gDC3yz9XwLhupzt+Dqpcn1nuzN/Rq/9tq3rJW6zeretS+kvLDyf2APXsJuOqpu0/18pyGWfXyn9kixq9W8/arhjAhyw39rqC0G9MzFDtdLXcz8Mtq/aUznMvH9bJfTS6untv1aH9Y9fyLOa7Ts6qn/uuql0/rYdVe28+e3quVn1CS7rsAZ7UbBiQzf5+ZR2Xmwyk/oryYkhB+MS1+pJAkaa5M8EqS5qz6MvyO6uXne1m3Gov05Mx8HuUy4oew4Zfxu9mw52A/PbbNeJzT1fOPGqbdVD3v1mL5xW22Xx/jtpf4f0Rpn/dpMW+falvn97C9jrqo/1YupvQ23SsiWiW363da71ucXagfq+nmGVWMj6Fcdv6zLrf3/er5yV0u/3NKsuDREfHAGZa9L+VHge9m5gY9K6uxkh/bcq1KZt6cmf+Tma+g9BrckQ3Pl7urbQ3qfQPrew0+oXr/N5txzNUufB64jXL5/Zcyc6Zegb0eMxhMXdXjeFYvK2XmbZn5rcx8M+WGfVv2uo0WOr0vHkqp2191UbezVl0R8HFgd9r3wj6Esr/nUX64aPW4AXh6ROzeZbn3UM4fWD/cQreWUpJy1wBnAkTEA4DnUBKE7WL8DuUz+tCGbdXHsH57Nc7wRqr30Nurl19sM+xJv32f8sPJE5vbwSqeZ1Qvz2yYVf/7Gc3v+2obT6S0Dd9n7urDJzXW2Uxtaq/t52zfq58DXkDp5X1WRHQc9iUzr8jMEyg9wC8FnhQRO3VaR5KkXpnglSTNSUTcnzJu3jSlt9R7Z1j+XhHxxBbTt2D9EA+3N8z6LXC/6vLVftsOeGdTHIuBgyk9gb7cMKs+9udLq55A9eV3a95Gg/qNVP60h5g+Uz3/c+N4fdXfR1cvP93D9jYwi/rfSHX56wnAvYF/atrOQyhDCNxFuQv9sHyuKvP1VeKq0T9RLqn9XDbd/KyDUyi96F4bEc9utUBEPKF+jLLc3O7jwNbAJ5p7QUfElg29bK+n1PFfRMPN76pj8BE2HHu3Pu+pbZJD9cuNm98z0Nt515OqZ2KNMtbpKxvnRcR+wNP7UMbvgP0oNzL6v12s8jHKOfChVgmX6hg0J38HUVfHU5KAr46IjRJNjWPsRsQ+jZ8nDeo9oDu+F7tQ/zz5v429vKuE9vsp3wVm/XnSg3+k/ADyDloPkVG/gdprMvPlrR6Um/nVb8QGQEQcGetvxLeBiHguZWzsmyg375pRRCyIiFdQxn9N4E2Z+Ydq9qGUHrYndIhxabVefexoKD2yL6T86HFsc1tWvf5UNf864N3dxDpXWW5C9p/AtpQrChq9jjJO+zcz87KGdX4JnFbNe23TOu+qtvWf2cXN+SLi8RGxtFXbXn0W1m+KdlbDrJso9dvu/dpr+9n1e7VZlpsIPpfyef3tiHhUw3r3i4hFLVbblnL+r6OMKy1JUt84Bq8kqWtRblQEJSmwPeUy4idRel79ADg4Z77z+9bA2RFxKaW31uWUG8rsS7lh1amZ2djL8gzKzUy+ERFnUe5K/ZPM/Cpzdxbw8oj4S0rPq50p4+ZtBrwyG25alpnnVOXvA/wgIr5FScL8NfBNWvfsPYMyHuinIuK/KT2Sbs7Mj7ULKDM/HxEHAs8DLoyIr7B+XNbdKb27Tmi3fhd6rf92VlB6Sr4uIh5H6dl13yruewOvy8xfzSHOnmTmmoh4IyUxc35E/Belx99TKDff+jnrEwbdbO+uiPg/lGO7KiK+S+m1ejvlWD8OeDDlnKkn4d4F/CXlnPhFRHyNcsx3o/SGeytwXGbeExEfpdTh6og4hfIeeiolyX4m63tB130ZWBsR36eMuxmU+n8c5Tj+b8OyZ1DGDj45yrjKvwcuz8x+J9xfS3nffLxKgv+UUid/S0mQH0iLO8j3IjO7vllbZv48Ig6lJHkujIhvUC4V34KSEHoy5ZxovNFe3+sqM38TES+iXIZ9ZkR8nVI39wEeTTkf6r1QPwrsEhHfoRzXO4G/oFyWfjnlx7NZy8zvRsT7KDewuiDK+J+3UXos7km5ZPxf51JGl3HcGBHvBTa6IVVETFMu+V+dnW+i92lKgvilEXFkZq6jjCd+VET8iDKG6w2UH+4eS3nfrwNe1eaHnYMaksPbsv4c2ZnyA9+y+pjB1Y8r9cTysR3289Io42JPU+p4VWbeVv3o8VVKkvjZ1bl2LaUNeXZV5q2UIWGu7FAHbUXEfdnwZnb1H4o+XQ0hAXB0Zv68YZm3V7G+OcoN235AaQcOpPwQ1ZzEhTIO+HeBj0bEEspVEX9J+cz6Beuv5pnJA4HPAh+LiLOBiyhXWexM+WHnAZTerv9YXyEz10bEOcCTI+KEqry7Ke3WT3ttP3t8r24kM0+tyvsyUIuIp2fmTyjDN/woIlZX27ui2uZzqv36aLa+waskSbOXmT58+PDhw0fHB+ULUuPjDuA3lMTSpyhfxjZrs26NapjT6vUWlGTD1yk9fv9A+VL+feBVwJZN629LuTP6lZQv60lJkjXGVusQ+3HVMgsbpi2sb4fyZfYUSs+g2ykJq2e22db21f5eX9XBBZQxGRc2x9WwzpspX4DvqJZZ065uGqZvRvkSfW4V0+1VXb+2VT13qoPm/e+1/mc4L7an3Mjokmr/bqbcMOwZbZZf07j/XZZRj39pl8s/g9LD7KYqpkspSaXtZzo322zv/pSeXxdUx2Fttb8nUcZSXNC0/AJK77cfVMveVi2/Enho03JvpiQ1fk9J9vwn5QZOrc7ZV1GSCJdVcdxIuRz5cODeTTFsTulJfxmlR+sG50er48D6noct67ndOUZJlp5cHfvbgO9Rxgd+S7XOQV0et3r57+5y+SvbHTtgUVWHl1fnwI3V8fsk8LS51lXT+kdV60y3mPco4D8oN3C6k9I789uUxGF9mecBJ1bnyFpKku8C4D3A/Xp4n8wU5wsoydzfUd7zF1IScVv1433adJ5c2WbevYBfsb4dWVBNP6F6/YYutn9atezfVK+fVNXV2ZTPszuq8/Biymf1ohbbOI4N27O7q3r/JfAVyvt3x6Z19q2WPb+LGF9ULXtK0/QtKEni/6V85t7dEMOpwANmU+cN21/YtF+tHq3O0x0pVw5cXp2n11B+JNm1Q1m7UZKz11TrXE65aeQOPcR7b+CF1XZ+SvmfYh3l/fpdyg9gf9JivYdSkuW/pfyAtMHnFj22n92+VxvO71qL9acp760bKT+6bU+5sudb1TbvqOqqVu1zzOVY+/Dhw4cPH60ekdnPeyhIkiRpvqt6170I2CMzL55peWk+qnrAfoOS4H9KZt4w4pAkSdKYcgxeSZIk9SwiNqtuPNU8fQllqJOLTO5K7WXmGcCrKVeSnB4RO4w4JEmSNKYcg1eSJEmzsSVwRUScSRnjeB3lUud9KZc5txq/U1KDzDw2Iu6ijPX6JMrwA5IkST1xiAZJkiT1LCI2p4y7+TRgV2AbyjiaZ1Fu5vSjEYYnSZIkzRsmeCVJkiRJkiRpTDkGryRJkiRJkiSNKRO8kiRJkiRJkjSmTPBKkiRJkiRJ0pgywStJkiRJkiRJY8oEryRJkiRJkiSNKRO8kiRJkiRJkjSmTPBKkiRJkiRJ0pgywStJkiRJkiRJY8oEryRJkiRJkiSNKRO8kiRJkiRJkjSmTPBKkiRJkiRJ0pgywStJkiRJkiRJY8oEryRJkiRJkiSNKRO8kiRJkiRJkjSmTPBKkiRJkiRJ0pgywStJkiRJkiRJY2rBqAPo5L73vW8uXLhwZOXfdtttbLvttiMrf9jc38nm/k4297c477zzfpOZ9xtBSBNh1O3uKM2391An1kVhPRTWw3rWRdFYD7a7czOO7a7vg/6yPvvL+uwv67O/+lGfndrdTTrBu3DhQs4999yRlV+r1Zienh5Z+cPm/k4293eyub9FRFw+/Ggmx6jb3VGab++hTqyLwnoorIf1rIuisR5sd+dmHNtd3wf9ZX32l/XZX9Znf/WjPju1uw7RIEmSJEmSJEljygSvJEmSJEmSJI0pE7ySJEmSJEmSNKZM8EqSJEmSJEnSmDLBK0mSJEmSJEljygSvJEmSJEmSJI0pE7ySJEmSJEmSNKaGmuCNiDdFxIURcUFEnBgRWw2zfEmSJEmSJEmaJENL8EbELsAbgMWZuSewOfCCYZUvSZIkSZIkSZNm2EM0LAC2jogFwDbA1UMuX5IkSZIkSZImxtASvJl5FfB+4NfANcAtmXnasMqXJEmSJEmSpEmzYFgFRcQOwIHA7sDNwJci4sWZ+bmm5ZYBywCmpqao1WrDCnEja9euHWn5w+b+Dtbqq27pOH/RLtsNtHyP72RzfyX1w8IVq1i+aB1LV6zaaN6ao/cfQUSSJEnSpm1hi/+dGw3j/+ihJXiBpwO/yswbACLiZOCvgA0SvJm5ElgJsHjx4pyenh5iiBuq1WqMsvxhc38Hq9WX5UZrDp4eaPke38nm/kqSJEmSND8NcwzeXwN7R8Q2ERHAEuBnQyxfkiRJkqQ5iYg3RcSFEXFBRJwYEVtFxO4RcU5EXBoRX4yILUcdpyRp/hjmGLznACcB5wOrq7JXDqt8SZIkSZLmIiJ2Ad4ALM7MPYHNgRcA/wJ8KDMfCtwEvGx0UUqS5pth9uAlM4/MzD0yc8/MfElm3jHM8iVJkiRJmqMFwNYRsQDYhnIT8adROjQBHA8cNKLYJEnz0FATvJIkSZIkjavMvAp4P2UIwmuAW4DzgNSrfL0AACAASURBVJszc1212JXALqOJUJI0Hw3zJmuSJEmSJI2tiNgBOBDYHbgZ+BKwXw/rLwOWAUxNTVGr1QYQ5eCsXbt27GLelFmf/WV99pf12b3li9Z1nF+r1QZenyZ4JUmSJEnqztOBX2XmDQARcTLwRGD7iFhQ9eLdFbiq1cqZuZLqXjSLFy/O6enpoQTdL7VajXGLeVNmffaX9dlf1mf3lq5Y1XH+moOnB16fDtEgSZIkSVJ3fg3sHRHbREQAS4CLgDOB51bLHAKcMqL4JEnzkAleSZIkSZK6kJnnUG6mdj6wmvKdeiXwNuDNEXEpsBPw6ZEFKUmadxyiQZIkSZKkLmXmkcCRTZMvAx4/gnAkSbIHryRJkiRJkiSNKxO8kiRJkiRJkjSmTPBKkiRJkiRJ0pgywStJkiRJkiRJY8oEryRJkiRJkiSNKRO8kiRJkiRJkjSmTPBKkiRJkiRJ0pgywStJkiRJkiRJY8oEryRJkiRJkiSNKRO8kiSNsYj4TERcHxEXNEzbMSJOj4hLqucdRhmjJEmSJGlwTPBKkjTejgP2a5q2AjgjMx8GnFG9liRJkiRNIBO8kiSNscw8C7ixafKBwPHV38cDBw01KEmSJEnS0CwYdQCSJKnvpjLzmurva4GpdgtGxDJgGcDU1BS1Wm3w0W2C1q5dO2/3vdHyReuY2ro8N5tv9eM5UVgP61kXhfUgSdKmxwSvJEkTLDMzIrLD/JXASoDFixfn9PT0sELbpNRqNebrvjdaumIVyxet4wOrN/4Xcc3B08MPaIQ8JwrrYT3rorAeJEna9DhEgyRJk+e6iNgZoHq+fsTxSJIkSZIGxASvJEmT51TgkOrvQ4BTRhiLJEmSJGmATPBKkjTGIuJE4HvAIyLiyoh4GXA0sG9EXAI8vXotSZIkSZpAQxuDNyIeAXyxYdKDgXdm5oeHFYMkSZMmM1/YZtaSoQYiSZIkSRqJoSV4M/Ni4DEAEbE5cBXw5WGVL0mSJEmSJEmTZlRDNCwBfpmZl4+ofEmSJEmSJEkae6NK8L4AOHFEZUuSJEmSJEnSRBjaEA11EbElcABwRJv5y4BlAFNTU9RqteEF12Tt2rUjLX/Y3N/BWr5oXcf5g47F4zvZ3F9JkiRJkuanoSd4gWcB52fmda1mZuZKYCXA4sWLc3p6eoihbahWqzHK8ofN/R2spStWdZy/5uDpgZbv8Z1s7q8kSZIkSfPTKIZoeCEOzyBJkiRJGjMR8YiI+HHD49aIeGNE7BgRp0fEJdXzDqOOVZI0fww1wRsR2wL7AicPs1xJkiRJkuYqMy/OzMdk5mOAvwBuB74MrADOyMyHAWdUryVJGoqhJngz87bM3CkzbxlmuZIkSZIk9dkS4JeZeTlwIHB8Nf144KCRRSVJmndGMUSDJEmSJEnj7gWsH35wKjOvqf6+FpgaTUiSpPloFDdZkyRJkiRpbEXElsABwBHN8zIzIyLbrLcMWAYwNTVFrVYbZJh9t3bt2rGLeVNmffaX9dlf1mf3li9a13F+rVYbeH2a4JUkSZIkqTfPAs7PzOuq19dFxM6ZeU1E7Axc32qlzFwJrARYvHhxTk9PDyXYfqnVaoxbzJsy67O/rM/+sj67t3TFqo7z1xw8PfD6dIgGSZIkSZJ680LWD88AcCpwSPX3IcApQ49IkjRvmeCVJEmSJKlLEbEtsC9wcsPko4F9I+IS4OnVa0mShsIhGiRJkiRJ6lJm3gbs1DTtt8CS0UQkSZrv7MErSZIkSZIkSWPKBK8kSZIkSZIkjSkTvJIkSZIkSZI0pkzwSpIkSZIkSdKYMsErSZIkSZIkSWPKBK8kSZIkSZIkjSkTvJIkSZIkSZI0pkzwSpIkSZIkSdKYMsErSZIkSZIkSWPKBK8kSZIkSZIkjSkTvJIkSZIkSZI0pkzwSpIkSZIkSdKYMsErSZIkSZIkSWPKBK8kSZIkSZIkjSkTvJIkSZIkSZI0pkzwSpIkSZIkSdKYMsErSZIkSZIkSWPKBK8kSZIkSZIkjamhJngjYvuIOCkifh4RP4uIJwyzfEmSJEmSJEmaJAuGXN5HgG9k5nMjYktgmyGXL0mSJEmSJEkTY2gJ3ojYDtgHWAqQmXcCdw6rfEmSJEmSJEmaNMMcomF34AbgsxHxo4g4NiK2HWL5kiRJkiRJkjRRhjlEwwLgscDrM/OciPgIsAL4h8aFImIZsAxgamqKWq02xBA3tHbt2pGWP2yz3d/VV93Scf6iXbabZUSD1W5/O+3PXPZl+aJ1HecP+lzzfJ5s7q8kSZIkSfPTMBO8VwJXZuY51euTKAneDWTmSmAlwOLFi3N6enpoATar1WqMsvxhm+3+Ll2xquP8NQf3vs1haLe/nfZnLvsy6nryfJ5s7q8kSdJwRMT2wLHAnkAChwIXA18EFgJrgOdl5k0jClGSNM8MbYiGzLwWuCIiHlFNWgJcNKzyJUmabyLiTRFxYURcEBEnRsRWo45JkqQJUL95+B7AXsDPKJ2XzsjMhwFn0KIzkyRJgzLMMXgBXg+cEBE/BR4DvHfI5UuSNC9ExC7AG4DFmbknsDnwgtFGJUnSeGu4efinodw8PDNvBg4Ejq8WOx44aDQRSpLmo2EO0UBm/hhYPMwyJUmaxxYAW0fEXcA2wNUjjkeSpHHXePPwvYDzgMOAqcy8plrmWmCq1cqb0j1nZsP7IPSX9dlf1md/WZ/d6+aeS4Ouz6EmeCVJ0nBk5lUR8X7g18DvgdMy87QRhyVJ0rhrd/PwP8rMjIhstfKmdM+Z2fA+CP1lffaX9dlf1mf3urnn0qDr0wSvJEkTKCJ2oFwuujtwM/CliHhxZn6uabmx7knUL/ZQKJYvWsfU1q17Icy3+vGcKKyH9ayLwnpoe/Pw6yJi58y8JiJ2Bq4fWYSSpHnHBK8kSZPp6cCvMvMGgIg4GfgrYIME77j3JOoXeygUS1esYvmidXxg9cb/Iq45eHr4AY2Q50RhPaxnXRTzvR4y89qIuCIiHpGZF7P+5uEXAYcAR1fPp4wwTEnSPGOCV5KkyfRrYO+I2IYyRMMS4NzRhiRJ0kSo3zx8S+Ay4KWUG5j/V0S8DLgceN4I45MkzTMmeCVJmkDVuIAnAecD64AfUfXUlSRJs9fh5uFLhh2LJElggleSpImVmUcCR446DkmSJEnS4Gw26gAkSZIkSZIkSbNjgleSJEmSJEmSxpQJXkmSJEmSJEkaUyZ4JUmSJEmSJGlMmeCVJEmSJEmSpDFlgleSJEmSJEmSxpQJXkmSJEmSJEkaUyZ4JUmSJEmSJGlMmeCVJEmSJEmSpDFlgleSJEmSJEmSxpQJXkmSJEmSJEkaUyZ4JUmSJEmSJGlMmeCVJEmSJEmSpDFlgleSJEmSJEmSxlRPCd6I+HFEvC4idhhUQJIkzSe2rZIkDY/triRpEvXag3cVcDhwdUScGBFLBhCTJEnziW2rJEnDY7srSZo4PSV4M/MdwIOA/wNsDqyKiF9FxDsj4k8HEaAkSZPMtlWSpOGx3ZUkTaKex+DN4uuZ+TzggcBK4O3AZRHxzYjYr926EbEmIlZXl8WcO/uwJUmaHHNpWyVJUm9sdyVJk2bBbFeMiL2BQ4HnA1cDnwV2Bk6KiGMz841tVn1qZv5mtuVKkjSp5tC2SpKkHtnuSpImRU8J3oi4P/D3wEuBhwCnAs/NzNMblvlP4HTAxlCSpBnYtkqSNDy2u5KkSdRrD94rgUuBTwPHt+mJeyHwwzbrJ3BaRCTwycxc2WP5kiRNmrm2rZIkqXu2u5KkidNrgndJZv6/Tgtk5q3AU9vMflJmXlX9anp6RPw8M89qXCAilgHLAKampqjVaj2G2D9r164dSfmrr7ql7bxFu2w3sHJnu7/LF63rOP+YE05pO2+Q+9PJ6qtuYWrr1rEtX9R+vbmcDzPV06DPtVGdz6Pi/k62CdvfubatkiSpe3NudyNiDfA74G5gXWYujogdgS8CC4E1wPMy86Y+xSxJUke9JnhvjIhHZ+ZPGydGxKMpDdtFnVbOzKuq5+sj4svA44GzmpZZSRnknsWLF+f09HSPIfZPrVZjFOUvXbGq7bw1B08PrNzZ7m+neGcyyP3pZOmKVSxftI4PrO7tLTCXeGeqp0HXxajO51FxfyfbhO3vnNpWSZLUk361u833llkBnJGZR0fEiur12/oTsiRJnW3W4/IrgT1bTH9kNa+tiNg2Iu5d/xt4BnBBj+VLkjRpZt22SpKkng2q3T0QOL76+3jgoDlsS5KknvTag/fRwA9aTP8h0OHCdgCmgC9HRL3cz2fmN3osX5KkSTOXtlWSJPWmH+1uq3vLTGXmNdX8aynffyVJGopeE7x3A60GTd0BiE4rZuZlwF49lidJ0qSbddsqSZJ61o92d6N7yzTOzMyskr8b2ZTuOTMbE3YfhJGzPvvL+uwv67N73dxzadD12WuC99vAOyLi7zLzboCIWAC8g6axdCVJUldsWyVJGp45t7tt7i1zXUTsnJnXRMTOwPVt1t1k7jkzGxN2H4SRsz77y/rsL+uze93cc2nQ9dlrgvdw4Gzg0og4u5r2JOBPgH36GZgkSfOEbaskScMzp3a3up/MZpn5u4Z7y/wjcCpwCHB09XzKAGKXJKmlnm6ylpkXU8Ys+jywY/U4AdgrM3/W//AkSZpstq2SJA1PH9rdKeDsiPgJZSzfVdW9ZY4G9o2IS4CnV68lSRqKXnvwUg0c/44BxCJJ0rxk2ypJ0vDMpd1td2+ZzPwtsGSOoUmSNCs9J3gjYhvgMcD9aeoBnJkn9ykuSZLmDdtWSZKGx3ZXkjRpekrwRsTTgROBnVrMTmDzfgQlSdJ8YdsqSdLw2O5KkiZRT2PwAh8BVgG7ZuZmTQ8bQkmSemfbKknS8NjuSpImTq9DNCwEDsjMqwcQiyRJ89FCbFslSRqWhdjuSpImTK89eL8DPGIQgUiSNE/ZtkqSNDy2u5KkidNrD95PAO+PiAcCq4G7Gmdm5vn9CkySpHnCtlWSpOGx3ZUkTZxeE7wnVc8rW8xzQHpJknpn2ypJ0vDY7kqSJk6vCd7dBxKFJEnzl22rJEnDY7srSZo4PSV4M/PyQQUiSdJ8NMi2NSK2B44F9qT0Sjo0M783qPIkSdrU+Z1WkjSJer3JGhHxrIj4WkRcFBG7VdNeHhFL+h+eJEmTb4Bt60eAb2TmHsBewM/mGqskSePO77SSpEnTU4I3Ig4G/gu4hHJpyxbVrM2Bw/sbmiRJk29QbWtEbAfsA3waIDPvzMyb5xatJEnjze+0kqRJ1GsP3sOBV2Tmm4B1DdO/Dzymb1FJkjR/DKpt3R24AfhsRPwoIo6NiG3nsD1JkiaB32klSROn15usPQxoNXbfWuA+cw9HkqR5Z1Bt6wLgscDrM/OciPgIsAL4h8aFImIZsAxgamqKWq02hyI3XauvuqXtvEW7bMfatWtb7nun9errTpLli9YxtXV5bnbMCae0XW/S6gFoe07MN9bDetZFMQH14HdaSdLE6TXBezXwcKB5YPp9gF/2JSJJkuaXQbWtVwJXZuY51euTKAneDWTmSmAlwOLFi3N6enoORW66lq5Y1XbemoOnqdVqtNr3TuvV150kS1esYvmidXxgdW//Ik5aPQBtz4n5xnpYz7ooJqAe/E4rSZo4vQ7RsBL4aEQ8sXq9W0QcArwP+Pe+RiZJ0vwwkLY1M68FroiIR1STlgAXzSlSSZLGn99pJUkTp6fuGZn5vuqmLacDWwFnAncA78/MfxtAfJIkTbQBt62vB06IiC2By4CXznF7kiSNNb/TSpImUa9DNJCZ74iI9wCPpPQAvigz1/Y9MkmS5olBta2Z+WNg8Vy3I0nSJPE7rSRp0vSc4AXIzNuBc/sciyRJ85ZtqyRJw2O7K0maJD0leCPi1E7zM/OAuYUjSdL8YtsqSdLw2O5KkiZRrz14f9v0egtgL2A34OS+RCRJ0vxi2ypJ0vDY7kqSJk6vN1lreXOWiPgAcGs324iIzSmXwlyVmc/ppXxJkiZNP9pWSZLUHdtdSdIk2qxP2/kk8Noulz0M+FmfypUkaVL10rZKkqS5sd2VJI2tfiV4H9HNQhGxK7A/cGyfypUkaVJ11bZKkqS+sN2VJI2tXm+y9tHmScDOwLOAz3SxiQ8DhwP37lDGMmAZwNTUFLVarZcQW1p91S1t5y3aZbu289auXduX8nu1fNG6tvNmime2+wpw/Y23cMwJp/S8bqd4ZzKX+u20rzNZvgimtu499nb1UzeXeppp27Mps9GozudRcX8n2yTtbx/aVkmS1KV+tLvNww5GxO7AF4CdgPOAl2Tmnf2LWpKkznq9ydqiptf3ADcAb2KGxjAingNcn5nnRcR0u+UycyWwEmDx4sU5Pd120a4tXbGq7bw1B7fffq1Wox/l92q28c513WNOOIUPrG59SnRat1OZM5kppk7mUi6UhGu7/Z2tQdXTbMtsNKrzeVTc38k2Yfs767ZVkiT1rB/tbn3YwftUr/8F+FBmfiEiPgG8DPj3PsQqSVJXer3J2lPnUNYTgQMi4tnAVsB9IuJzmfniOWxTkqSxNse2VZIk9WCu7W7DsIPvAd4cEQE8DXhRtcjxwFGY4JUkDVG/xuCdUWYekZm7ZuZC4AXAt0zuSpIkSZLGSH3YwXuq1zsBN2dmfTy2K4FdRhGYJGn+6nUM3jOB7GbZzHzarCKSJGkesW2VJGl45tLudjvs4Azl9/2eM8M0SfdB2BRYn/1lffaX9dm9me65VKvVBl6fvQ5A+jPgYOBa4Jxq2uOBBwCfB+7uZiOZWQNqPZYtSdIk6kvbKkmSujKXdnejYQeBjwDbR8SCqhfvrsBV7TYwiHvODNOE3Qdh5KzP/rI++8v67N5M91xac/D0wOuz1wTvHZQxhQ7LzD/+6hkRHwYiMw/rZ3CSJM0Dtq2SJA3PrNvdzDwCOKJafhp4S2YeHBFfAp4LfAE4BDhlcOFLkrSxXsfg/XvgY40NYeXjwEv6E5IkSfOKbaskScMziHb3bZQbrl1KGZP303OIT5KknvXagzeARcAvmqYv6k84kiTNO7atkiQNT1/a3cZhBzPzMsowD5IkjUSvCd7PAMdGxMOA71fT9qbcRfSz/QxMkqR5wrZVkqThsd2VJE2cXhO8hwPXA4cB762mXQMcDXygj3FJkjRf2LZKkjQ8truSpInTU4I3M+8B3ge8LyLuU027dRCBSZI0H9i2SpI0PLa7kqRJ1GsPXgAiYjHwEOBr1ettgTsyc10fY5Mkad6wbVW3Fq5Y1XH+mqP3H1Ik/dFpf8ZtXySND9tdSdIk6SnBGxFTwCmUAeQTeBhwGfBB4A+Uy1wkSVKXbFslSRoe211J0iTarMflPwRcB+wE3N4w/UvAM/oVlCRJ84htqyRJw2O7K0maOL0O0bAEWJKZN0VE4/RfAn/at6gkSZo/bFslSRoe211J0sTptQfv1sCdLabfj3I5iyRJ6o1tqyRJw2O7K0maOL0meM8Clja8zojYHHgbcEa/gpIkaR6xbZUkaXhsdyVJE6fXIRoOB74dEY8D7gV8AHgUsB3wxD7HJknSfGDbKknS8NjuSpImTk89eDPzImAR8F3gNGArymD0f56Zv+x/eJIkTTbbVkmShsd2V5I0ibruwRsRWwBnA3+fmUcOLiRJkuYH21ZJkobHdleSNKm67sGbmXcBuwM5uHAkSZo/bFslSRoe211J0qTq9SZrxwOvGEQgkiTNU7atkiQNj+2uJGni9HqTtW2BgyNiX+A84LbGmZn5hn4FJknSPGHbKknS8NjuSpImTlcJ3oh4NHAh8GfA+dXkBzct5mUukiR1ybZVkqThsd2VJE2ybnvw/gjYOTOfChARq4CXZ+Y1A4tMkqTJZtsqSdLw2O5KkiZWt2PwRtPrJwNb9zkWSZLmE9tWSZKGx3ZXkjSxer3JWl1z4yhJkubGtlWSpOGx3ZUkTYxuE7zJxuMROT6RJEmzZ9sqSdLw2O5KkiZWt2PwBvC5iLijer0V8KmIuL1xocw8oO0GIrYCzgLuVZV7UmYe2XvIkiRNhDm3rZIkqWu2u5KkidVtgvf4ptefm0VZdwBPy8y1EbEFcHZEfD0zvz+LbUmSNO760bZKkqTu2O5KkiZWVwnezHzpXAvKzATWVi+3qB5eEiNJmpf60bZKkqTu9KvdbXdlakTsDnwB2Ak4D3hJZt7ZjzIlSZrJbG+yNisRsXlE/Bi4Hjg9M88ZZvmSJM03Vdv7o4j42qhjkSRpAtSvTN0LeAywX0TsDfwL8KHMfChwE/CyEcYoSZpnuh2ioS8y827gMRGxPfDliNgzMy9oXCYilgHLAKampqjVanMud/midW3nddr+9TfewjEnnNJ2/qJdtms7b/VVt3SMqdO6s413pnU77QvA1Nbt1+9UbqcyZzJTTLOtp2502t/ZGlQ9zbbMRmvXru3L+2lcuL+Tbb7t7xwdBvwMuM+oA5Ekadx1uDL1acCLqunHA0cB/z7s+CRJ89NQE7x1mXlzRJwJ7Adc0DRvJbASYPHixTk9PT3n8pauWNV23pqD22//mBNO4QOr21dRp3U7lTmXdTut1025nSxftK7t/s5lX+dikOV22t/ZGkU9zXRO1NVqNfrxfhoX7u9km2/7O1sRsSuwP/Ae4M0jDkeSpIkQEZtThmF4KPBvwC+BmzOz3qPjSmCXEYUnSZqHhpbgjYj7AXdVyd2tgX0pl7FIkqTB+DBwOHDvUQciSdKkaL4yFdij23UHccXqMHkVVX9Zn/1lffaX9dm9ma7YrtVqA6/PYfbg3Rk4vvq1czPgvzLT8QAlSRqAiHgOcH1mnhcR0x2WG+svmt2aafijdv9wzfTP2myHcoLOwznNZYiiTsdwpiGkli+a3RBGczlv5jK81FzMdHz8UlNYD+tZF4X1sF7DlalPALaPiAVVL95dgavarNP3K1aHyauo+sv67C/rs7+sz+51cxX/oOtzaAnezPwp8OfDKk+SpHnuicABEfFsYCvgPhHxucx8ceNC4/5Fs1szDX/U7h+uuQyvM5dhlUYxDFTdbIYw6na4oFYGOdRTJzPF7JeawnpYz7oo5ns9dLgy9UzgucAXgEOAwf1CJUlSk81GHYAkSeq/zDwiM3fNzIXAC4BvNSd3JUlSz3YGzoyInwI/BE6vrkx9G/DmiLgU2An49AhjlCTNMyO5yZokSZIkSeOm3ZWpmXkZ8PjhRyRJkgleSZImXmbWgNqIw5AkSZIkDYBDNEiSJEmSJEnSmDLBK0mSJEmSJEljygSvJEmSJEmSJI0pE7ySJEmSJEmSNKZM8EqSJEmSJEnSmDLBK0mSJEmSJEljygSvJEmSJEmSJI0pE7ySJEmSJEmSNKZM8EqSJEmSJEnSmDLBK0mSJEmSJEljygSvJEmSJEmSJI0pE7ySJEmSJEmSNKZM8EqSJEmSJEnSmDLBK0mSJEmSJEljygSvJEmSJEmSJI0pE7ySJEmSJEmSNKZM8EqSJEmSJEnSmDLBK0mSJEmSJEljasGoA5AkSZNl4YpVbeetOXr/IUYyWp3qYZDrboombX8kSZKkTYk9eCVJkiRJkiRpTA0twRsRu0XEmRFxUURcGBGHDatsSZIkSZIkSZpEwxyiYR2wPDPPj4h7A+dFxOmZedEQY5AkSZIkSZKkiTG0HryZeU1mnl/9/TvgZ8AuwypfkiRJkqS5aHdlakTsGBGnR8Ql1fMOo45VkjR/jGQM3ohYCPw5cM4oypckSZIkaRbqV6Y+EtgbeG1EPBJYAZyRmQ8DzqheS5I0FMMcogGAiPgT4L+BN2bmrS3mLwOWAUxNTVGr1eZc5vJF69rO67T9qa1nv26n9eay7kz1MVO5nXTa32NOOKVDmbMuckaDLHem4zsbczknBlEmwOqrbgHK/jbX56Jdtutq3VY6rdtpvZnMFFO31q5d25fPj3Hh/kqSJA1eZl4DXFP9/buIqF+ZeiAwXS12PFAD3jaCECVJ89BQE7wR/7+9e4+WrCzvPP79hUZFTbxhWgLENhN0orai9hizGLWFYYZEA67RMBpD6NDajjEKmZ7MdBzHiWZlLYzRjCGOCUsIBNFgUAMRRHuILYkJGCUoNx0Ig9JcvXEbBGx95o/aLcWhqk51n6ravau+n7Vqnap9fd5379pv1XP2+1b2ppfcPbOqPjZomao6GTgZYN26dbV+/foV73fDlvOGzrv+NcO3f9KZ5/Duy4dX0ah1R+1zJeuOWm+c/Y6yee2OkeWdN9Mo70rOiWnss3+/g8q7kvNpWmVdLqZxbdu2jUlcP7rC8kqSJM3Wkp6pq5vkL8AtwOqWwpIkLaCZZfOSBDgFuLqq3jOr/UqSJEmSNElLe6b2vu72VFUlqSHrTbzH6izZi2qyrM/Jsj4ny/oc3zi9+Kddn7O8XfMQ4Bjg8iSXNdPeUlXnzzAGSZIkSZJ225Ceqbcm2a+qbk6yH3DboHWn0WN1luxFNVnW52RZn5NlfY5vnF78067PmSV4q+rvgCy7oCRJkiRJe6ARPVPPBY4FTmz+Dv8xEUmSJmxxBlyVJEmSJGllBvZMpZfY/UiSjcDXgKNbik+StIBM8EqSJEmSNIZleqYeNstYJEna6UfaDkCSJEmSJEmStHtM8EqSJEmSJElSR5nglSRJkiRJkqSOMsErSZIkSZIkSR1lgleSJEmSJEmSOsoEryRJkiRJkiR1lAleSZIkSZIkSeooE7ySJM2hJAcm+UySq5JcmeT4tmOSJEmSJE3eqrYDkCRJU7ED2FxVlyb5UeCLSbZW1VVtByZJkiRJmhzv4JUkaQ5V1c1VdWnz/C7gamD/dqOSJEmSJE2aCV5JkuZckjXAc4BL2o1EkiRJkjRpDtEgSdIcS/Jo4KPACVV154D5m4BNAKtXr2bbtm0r3ufmtTuGzjvpzHNWvP1B1u7/mJHzl4tp9T6DY9u8dsWhzdyoOh6nPKv3viclkgAAE0xJREFUGV1fu7rPcfc7a8vFPOycWM5y52LX3H333UOvC5ffeMfQ9eatHmB0XSwS60GSpD2PCV5JkuZUkr3pJXfPrKqPDVqmqk4GTgZYt25drV+/fsX73bDlvBVvY1dd/5r1I+cvF9PmtTt49+V+LALrYqfdrYflzsWu2bZtG8OuC6PeV/NWDzC6LhaJ9SBJ0p7HIRokSZpDSQKcAlxdVe9pOx5JkiRJ0nSY4JUkaT4dAhwDHJrksubxC20HJUmSJEmaLPvfSZI0h6rq74C0HYckSZIkabq8g1eSJEmSJEmSOsoEryRJkiRJkiR1lAleSZIkSZIkSeooE7ySJEmSJEmS1FEmeCVJkiRJkiSpo0zwSpIkSZIkSVJHzSzBm+TUJLcluWJW+5QkSZIkaVIGfa9N8vgkW5Nc0/x9XJsxSpIWzyzv4D0NOGKG+5MkSZIkaZJO46Hfa7cAF1bVQcCFzWtJkmZmZgneqroI+Pas9idJkiRJ0iQN+V57FHB68/x04OUzDUqStPBWtR3AUkk2AZsAVq9ezbZt21a8zc1rdwydN2r7q/fZ/XVHrbeSdU8685yR2928duTskZYr77yZRnlHHZ+VHJvd3Wf/fgeVdyXn07TKulxMa/d/zFjbufvuuydy/QC4/MY7Rs4fN6Zp6i/vNOMdte1R2510TJM8vpIkSSu0uqpubp7fAqxuMxhJ0uLZ4xK8VXUycDLAunXrav369Sve5oYt5w2dd/1rhm//pDPP4d2XD6+iUeuO2udK152WzWt3jCzvvLG83TTqvdNv27ZtTOL6ASt7P89Kf3mnGe/uXk8nHdMkj68kSdKkVFUlqWHzp3FD0yz5T/bJsj4ny/qcLOtzfOPc5Dnt+ux+tkeSJEmSpPbcmmS/qro5yX7AbcMWnMYNTbPkP9kny/qcLOtzsqzP8Y1zQ9O063OWP7ImSZIkSdK8ORc4tnl+LDB6zDFJkiZsZgneJB8G/gF4WpLtSTbOat+SJEmSJK3UkO+1JwKHJ7kG+DfNa0mSZmZmQzRU1atntS9JkiRJkiZtxPfaw2YaiCRJfRyiQZIkSZIkSZI6ygSvJEmSJEmSJHWUCV5JkiRJkiRJ6qiZjcErSZI0LWu2nNd2CBIw+ly8/sSXTm3d3bXce2fz2h1s8P3VOcsd12mdT5IkqR3ewStJkiRJkiRJHeUdvJIkSZIkSZI0xJ7eY9A7eCVJkiRJkiSpo0zwSpIkSZIkSVJHmeCVJEmSJEmSpI4ywStJkiRJkiRJHWWCV5IkSZIkSZI6alXbAUiSJEmSJElSm9ZsOa/tEHabd/BKkiRJkiRJUkeZ4JUkSZIkSZKkjjLBK0mSJEmSJEkdZYJXkiRJkiRJkjrKBK8kSZIkSZIkddSqtgOQJEmSJEmSpGlas+W8tkOYGu/glSRJkiRJkqSOMsErSZIkSZIkSR3lEA2SJEmSJEmSOm+eh2EYxTt4JUmSJEmSJKmjTPBKkiRJkiRJUkfNNMGb5IgkX01ybZIts9y3JEmLxnZXkqTZsd2VJLVlZmPwJtkLeB9wOLAd+Mck51bVVbOKQZKkRWG7K0nS7LTZ7o4ab/L6E1867d1Lreo//zev3cGGvtee/1oks7yD9/nAtVV1XVXdD/wFcNQM9y9J0iKx3ZUkaXZsdyVJrZllgnd/4Ia+19ubaZIkafJsdyVJmh3bXUlSa1JVs9lR8krgiKp6bfP6GOBnq+o3liy3CdjUvHwa8NWZBDjYvsA3W9z/rFne+WZ555vl7XlyVT1x1sHsiTra7rZp0d5Do1gXPdZDj/XwAOuip78ebHcbC9Tu+j6YLOtzsqzPybI+J2sS9Tm03Z3ZGLzAjcCBfa8PaKY9SFWdDJw8q6BGSfKFqlrXdhyzYnnnm+Wdb5ZXA3Su3W2T59QDrIse66HHeniAddFjPQy1EO2ux3+yrM/Jsj4ny/qcrGnX5yyHaPhH4KAkT0nyMOBVwLkz3L8kSYvEdleSpNmx3ZUktWZmd/BW1Y4kvwF8CtgLOLWqrpzV/iVJWiS2u5IkzY7triSpTbMcooGqOh84f5b7XKHOdp3ZTZZ3vlne+WZ59RAdbHfb5Dn1AOuix3rosR4eYF30WA9DLEi76/GfLOtzsqzPybI+J2uq9TmzH1mTJEmSJEmSJE3WLMfglSRJkiRJkiRN0MIneJOcmuS2JFcMmZ8kf5Tk2iRfTvLcWcc4SWOUd32SO5Jc1jzeNusYJynJgUk+k+SqJFcmOX7AMnNzjMcs79wc4ySPSPL5JF9qyvv2Acs8PMlZzfG9JMma2Ue6cmOWdUOSb/Qd29e2EeskJdkryT8l+cSAeXNxbNWuca6bi2Cca8wiGXXtWSRJrk9yedOmfKHteNqS5LFJzk7ylSRXJ/m5tmNqQ5Kn9X3GuCzJnUlOaDsuTU+S45Nc0bQLJzTT3tW8F76c5ONJHtt2nF0xqD775m1OUkn2bSu+rhlWn0ne1JyjVyb5/TZj7JIh7/eDk1y883NAkue3HeeealCuLcnjk2xNck3z93HN9KnkoBY+wQucBhwxYv7PAwc1j03A+2cQ0zSdxujyAvxtVR3cPN4xg5imaQewuaqeDrwAeGOSpy9ZZp6O8Tjlhfk5xvcBh1bVs4GDgSOSvGDJMhuB71TVTwN/CLxzxjFOyjhlBTir79h+YLYhTsXxwNVD5s3LsVW7xr1uzrtxrzGLYtS1Z9G8pGlT1rUdSIveC1xQVf8SeDYLem5U1Vd3fsYAngfcA3y85bA0JUmeCbwOeD698/5lSX4a2Ao8s6qeBfwf4Lfbi7I7RtQnSQ4E/i3w9fYi7JZh9ZnkJcBRwLOr6hnAH7QYZmeMOD9/H3h7c91/W/Nag53GQ3NtW4ALq+og4MLmNUwpB7XwCd6qugj49ohFjgL+vHouBh6bZL/ZRDd5Y5R3rlTVzVV1afP8LnofyPdfstjcHOMxyzs3mmN2d/Ny7+axdGDxo4DTm+dnA4clyYxCnJgxyzpXkhwAvBQYlqiei2Ordi3adXOYRbzGDDPGtUcLJMljgBcBpwBU1f1VdXu7Ue0RDgP+uaq+1nYgmpqfAS6pqnuqagfwWeDfV9Wnm9cAFwMHtBZhtwysz2beHwL/hQVtd3fTsPp8A3BiVd0HUFW3tRhjlwyrzwJ+rFnmMcBNLcW3xxuSa+v/vno68PK+6RPPQS18gncM+wM39L3ezvx/8fu5povmJ5M8o+1gJqXpvv0c4JIls+byGI8oL8zRMW660V4G3AZsraqhx7dprO4AnjDbKCdjjLICvKLp5nF2czdAl/1Peh92fzBk/twcW+0Zlrluzr0xrzGLYLlrzyIp4NNJvphkU9vBtOQpwDeAP2uG7fhAkke1HdQe4FXAh9sOQlN1BfDCJE9I8kjgF4Clny2PAz4588i6aWB9JjkKuLGqvtRueJ0z7Px8ajP9kiSfTfKvWo2yO4bV5wnAu5LcQO9uaO/Y3zWrq+rm5vktwOrm+VRyUCZ4tdSlwJObLponAX/VcjwTkeTRwEeBE6rqzrbjmbZlyjtXx7iqvt90GTkAeH7TvWQujVHWvwbWNF3mtvLAfws7J8nLgNuq6ottx6LFsGjtxCCLdD0dxmvPQ/zrqnouva6Eb0zyorYDasEq4LnA+6vqOcD/44EulgspycOAI4G/bDsWTU9VXU1v+KtPAxcAlwHf3zk/yX+jN8zRma0E2DFD6vPhwFvodX3XLhhxfq4CHk9v2K3fAj5iD7/ljajPNwC/WVUHAr9J05tFu66qiinfpW+Cd3k38uD/VB7QTJtLVXXnzi6aVXU+sHfXB3pPsje9L+1nVtXHBiwyV8d4ufLO4zEGaLpLfoaHjnvzw+ObZBW9riXfmm10kzWsrFX1rZ3dkeh1LX7erGOboEOAI5NcD/wFcGiSDy5ZZu6OrdoxRjuxUEZcTxfBONeehVFVNzZ/b6M31uoi/rjKdmB73x3tZ9NL+C6ynwcurapb2w5E01VVp1TV86rqRcB36I25S5INwMuA1zRJC41hQH1eSa+XwJeaducA4NIkT2oxzM4Ycn5uBz7WdH3/PL3eOJ3/rjsLQ+rzWGDnZ+O/ZDE/B6zErTuHXmj+7hwyZCo5KBO8yzsX+NXmV+5eANzRd4v13EnypJ3/4Wp+IfFH6HDCpCnLKcDVVfWeIYvNzTEep7zzdIyTPDHNL/cm2Qc4HPjKksXOpdcwAbwS+JsufhAdp6xLxu05kg7/CExV/XZVHVBVa+h1A/2bqvqVJYvNxbFVu8ZsJ+bemNfTuTfmtWchJHlUkh/d+ZzeDwBdMXqt+VNVtwA3JHlaM+kw4KoWQ9oTvBqHZ1gISX68+fuT9Mbj/FCSI+gNY3NkVd3TZnxdM6A+T6+qH6+qNU27sx14bnPd0TIGnZ/0eqe+pJn+VOBhwDfbirFLhtTnTcCLm0UOBa5pJ7rO6v++eixwTt/0ieegVq10A12X5MPAemDfJNuB/0Hvh0Woqj8Bzqc3/si19H4p9tfaiXQyxijvK4E3JNkBfBd4VccTJocAxwCXN+MKQq8bzE/CXB7jcco7T8d4P+D0JHvRS1R/pKo+keQdwBeq6lx6iZszklxLb9DzV7UX7oqMU9Y3JzmSXne5bwMbWot2Sub02KpdA6+bTQ+HRTLwGtNyTGrXauDjzf+EVwEfqqoL2g2pNW8CzmyGJriObn9WXJEm2X848Pq2Y9FMfDTJE4DvAW+sqtuT/DG9oQW2NteHi6vqP7YZZIc8pD7bDqjjBp2fpwKnJrkCuB84tsPfdWdtUH2+Dnhv01vyXmBRx+Nf1pBc24n0hgnZCHwNOLpZfCo5qHiuS5IkSZIkSVI3OUSDJEmSJEmSJHWUCV5JkiRJkiRJ6igTvJIkSZIkSZLUUSZ4JUmSJEmSJKmjTPBKkiRJkiRJUkeZ4JUkSZIkSZKkjjLBK81QktOSVJJTBsx7ZzPvE0umr05yb5KvJ3nIezbJ9c16leSeJFck2dQ3f0Pf/KWPR0ynpJIkzVZfG1tJdjTt5vuTPK5vmZ1t5jED1r+kmfef+6Y9JckHk2xPcl+Sm5Kcl+Q5A7ZpOyxJ0piWa7eT/HqSrUk+l+RTSR7fdszSnswErzR7NwBHJ3nUzglJVgG/Cnx9wPLHAn8N3Av8uyHbfAewH/As4K+AP03yH/rm39PMf9Cjqu5dWVEkSdqj/G96bdwa4LXALwL/a8kyNwDH9U9I8kzgmcC3+qbtDWwFnggcDTwVeAXweWDpl0zbYUmSdt2odvsDVXV4VR0C7AX8bCsRSh2xqu0ApAX0ZeAn6H1Z/LNm2kvpJXAvAp6wZPnjgN8CDgY2Ap8csM27quqW5vlbkxwNvBw4q5lWffMlSZpX9/W1d9uTnAVsWLLMh4ATkvxUVV3XTNsInA28uG+5ZwD/Ajiiqq5tpn0N+IcB+7UdliRp1w1tt6vqfoCmV8ytwAWtRCh1hHfwSu04hQffPXQcvWRv9S+U5IX0Er4XAB8EXpbkiWNs/15g78mEKklS9yT5KeAI4HtLZn2TXs+YX2uWexjwK/Ta5n7fAH4AvKLpabMrbIclSdoFS9vtJI9I8j7gQOCYqqpR60uLzgSv1I4PAeuSHJTkSfQastMGLLcROKuqvldV/5det9Bjh200yaokG4C1wIV9sx6V5O4lj7+fVGEkSdpDHNG0cd8F/hl4OvDOAcudChzbjG1/JHB7VV3Uv0BV3Qi8GXgbcHuSzyb53STPGLZz22FJknbJqHb7vcAvA4cDf5/klS3FKHWCQzRILaiq7yT5OL07d28HtlXV15P8cJkkPwb8EnBo36pnAP8J+IMlm/y9JL8DPBy4H3gX8Kd98++hN8RDv/tWXhJJkvYoFwGbgH2A19EbYuGPBiz3KSD0vjRupJfwfYiqel+SPwdeQm/sv6OALUmOq6oz+ha1HZYkadcNbber6vXA69sLTeoWE7xSe04FTgfupnd30FK/DDwS+Fx/4hfYK8khVfW5vmnvode19B7g5gHdV6pv/EBJkubVPX3t3ZuTfAb478Dv9C9UVT9IcjrwFuAF9JK8A1XVXcC5wLlJ3kovOfy79P7pupPtsCRJu26sdlvS8hyiQWrPhfTu8tmX3i9uL7UR+GN6d/z0P87joV9Ev1VV11bVTY5NJEnSD70d+K9JfmLAvFOBFwJbq+qmcTbWtLFfAR69ZJbtsCRJKzeq3ZY0gnfwSi2pqkryLCBV9aBums30dcDGqrpiybwzgFOSHN/cVTSONGP9LvWNqvr+7sQvSdKerqq2JbkKeCvw60vmXZdkX+C7g9ZNcjC9L5pnAFfR+6fsi+kNr/ThXQzFdliSpGWMarcljeYdvFKLququqrpzwKzXAtdU1ZcHzPsEvffuq3dhV48Ebh7weMquRSxJUue8G9iY5MlLZ1TVt6tqYIIX2A5cR28YpYuBy4DN9MbBf9MuxmA7LEnSeIa225KGi73IJEmSJEmSJKmbvINXkiRJkiRJkjrKBK8kSZIkSZIkdZQJXkmSJEmSJEnqKBO8kiRJkiRJktRRJnglSZIkSZIkqaNM8EqSJEmSJElSR5nglSRJkiRJkqSOMsErSZIkSZIkSR1lgleSJEmSJEmSOur/A6ztJrMM6K/tAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1747.48x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "WDaqlTE1ylpo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}